With the combination of advances in communication frameworks, processing power, cheaper and more abundant data storage, automated transaction recordation, data mining techniques, more efficient storage schema, as well as enterprise globalization and numerous other factors, more data than ever before is being collected and analyzed for a variety of purposes. Common goals of analyzing this ever-increasing data include efforts to increase efficiency, optimize transactions, problem-solving, and so forth.
However with so much data available, associated databases have increased in size tremendously. As a result, making sense of that data is no longer a trivial task, and solutions to a given analytical task can be hidden in subtle or complex ways within vast data sets. For example, in the field of Enterprise Resource Planning (ERP) as well as other areas, a growing amount of human and computer-based resources are being devoted to data analysis. Conventional tools for visualizing data in a desktop environment have evolved considerably. Thus, it can be a simple matter to visually chart an underlying relational database in order to arrive at a given hypothesis as to a particular problem or inefficiency. However, open-ended analysis on complex data sets often leads to complex exploration and/or multiple competing hypotheses, for which conventional tools are not adequately equipped to handle in a convenient or efficient manner.